Informatics Tools for High-throughput Analysis of Cancer Mutations

用于癌症突变高通量分析的信息学工具

• 批准号: 8606625
• 负责人: Rachel Karchin
• 金额: $ 29万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-17 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8606625
• 关键词: Address; Binding Sites; Bioinformatics; Biological; Cancer Center; Cancer Prognosis; Categories; Classification; Collaborations; Communities; Computer Analysis; Computer software; Country; Data; Databases; Development; Diagnostic; Disease; Doctor of Medicine; Ensure; Gene Frequency; Gene Mutation; Genes; Genetic; Genetic Polymorphism; Genomics; Heterogeneity; Histocompatibility Testing; Housing; Image; Imagery; Informatics; Internet; Letters; Machine Learning; Malignant Neoplasms; Maps; Methods; Missense Mutation; Molecular; Mutagenesis; Mutate; Mutation; Mutation Analysis; Occupations; Pathway Analysis; Pattern; Pharmaceutical Preparations; Population; Positioning Attribute; Privacy; Production; Proteins; Publications; Qualifying; RNA Splicing; Research Infrastructure; Research Personnel; Resources; Roentgen Rays; Scientist; Secure; Site; Source; Structure; Technology; The Cancer Genome Atlas; Transcript; Translations; Tumor Tissue; Untranslated Regions; Update; Variant; Work; anticancer research; base; cancer cell; cancer classification; cancer genome; cancer genomics; cancer therapy; cohort; data exchange; design; exome; exome sequencing; experience; high throughput analysis; improved; insertion/deletion mutation; insight; interest; microbial alkaline proteinase inhibitor; next generation sequencing; novel strategies; prognostic; protein function; protein structure; public health relevance; software development; tool; tumor; user-friendly; web interface; web services

PROJECT SUMMARY Large tumor exome sequencing projects have identified a very large number of mutations whose cancer relevance is not yet understood. To begin to address this need, our team has produced two web applications for high-throughput computational analysis of cancer mutations: the Cancer-Related Analysis of VAriants Toolkit (CRAVAT) and the Mutation Position Imaging Toolbox (MuPIT). CRAVAT accepts millions of mutations in a single batch upload and maps mutations from genomic coordinates to annotated transcripts and proteins. MuPIT currently accepts batch uploads of up to 2500 SNVs and maps from genomic coordinates onto X-ray crystal structures of proteins from Protein Data Bank (PDB). We propose to combine and harden CRAVAT and MuPIT into a single web application, in which we will substantially improve the tools, user interface, software infrastructure, integration with external data resources and tools used by the community, and support for protected data. The scope of all tools in the web application will be broadened to handle analysis of the full range of small-scale mutation consequence types found in cancer exomes. CRAVAT analysis identifies mutations most likely to have deleterious impact on protein function and those that are most likely to confer a selective advantage to cancer cells (drivers), using classifiers developed by our team. Classifier scores are supplemented with annotations, including population allele frequencies, previous occurrence in tumor tissue types, and gene functional categories, enabling filtering (e.g. removing polymorphisms) and prioritization. Gene-level annotation and scoring, by aggregation of classifier scores from mutations in a cohort is also provided. MuPIT maps mutations from genomic positions onto to protein structures and provides interactive viewing of mutations in the context of protein structure, and in relation to a variety of annotations. To enable prioritization of interesting mutations and genes, the application provides a preview describing each structure and all available annotations (e.g., binding sites, experimental mutagenesis results, polymorphic and disease- associated variants that have been previously documented). After selecting a PDB of interest, the user is led to an interactive visualization page. An enhanced Jmol applet displays all SNVs mapped onto the structure. Frequently, many SNVs in the input list can be mapped onto a single structure, revealing clustering patterns around key functional sites. Based only on word-of-mouth, since the debut of the two applications in August 2012, CRAVAT has been utilized by 129 unique users from 39 countries, and it has analyzed 1,136 submitted jobs, totaling 27.9 million mutations. MuPIT has been utilized by 242 unique users from 25 countries, with 720 submitted jobs. (Source: Google Analytics).

项目摘要 大型肿瘤外显子组测序项目已经确定了大量突变，其癌症 相关性尚未理解。为了开始满足这一需求，我们的团队生产了两个Web应用程序 对于癌症突变的高通量计算分析：与癌症相关的变体分析 工具包（cravat）和突变位置成像工具箱（MUPIT）。 Cravat接受数百万突变 在单个批处理中，从基因组坐标到注释的转录本和蛋白质中映射突变。 MUPIT目前接受最多2500个SNV的批次上传，并从基因组坐标上添加到X射线上 蛋白质数据库（PDB）的蛋白质晶体结构。我们建议结合和硬化 和mupit到一个单一的Web应用程序中，在该应用程序中，我们将基本上改进工具，用户界面， 软件基础架构，与社区使用的外部数据资源和工具集成以及支持 用于受保护的数据。 Web应用程序中所有工具的范围将扩大以处理完整的分析 癌症外事物中发现的小规模突变后果类型的范围。 Cravat分析确定最有可能对蛋白质功能产生有害影响的突变，以及 使用我们开发的分类器，最有可能赋予癌细胞（驱动程序）的选择性优势 团队。分类器得分补充了注释，包括人口等位基因频率，以前 发生在肿瘤组织类型和基因功能类别中，使过滤（例如去除 多态性）和优先级。基因级注释和评分，通过汇总分类器得分的分数 还提供了队列中的突变。 MUPIT映射突变从基因组位置到蛋白质结构，并提供交互式观察 在蛋白质结构的背景下的突变，以及各种注释。启用 有趣的突变和基因的优先级，应用提供了描述每个结构的预览 以及所有可用的注释（例如，结合位点，实验诱变结果，多态性和疾病 - 以前已记录的相关变体）。选择了感兴趣的PDB后，用户被带到 交互式可视化页面。增强的Jmol小程序显示所有映射到结构上的SNV。 通常，输入列表中的许多SNV可以映射到单个结构上，揭示了聚类模式 围绕关键功能站点。 自2012年8月这两个申请的首次亮相以来，Cravat一直是基于口碑 由来自39个国家 /地区的129个唯一用户使用，并分析了1,136个提交的工作，总计2790万 突变。来自25个国家 /地区的242位唯一用户使用了MUPIT，并有720个提交的工作。 （来源： Google Analytics（分析））。